Fuzzy based design of digital IIR filter using ETLBO

Abstract

In this paper, a population-based robust enhanced teaching{learnin optimization (ETLBO) algorithm with reduced computational effort and high consistency is applied to design stable digital innite-impulse response (IIR) �lters in a multiobjective framework. Furthermore, a decision-making methodology based on fuzzy set theory is applied to handle nonlinear and multimodal design problems of the IIR digitallter. The original teaching{learnin optimization (TLBO) algorithm has been remodeled by merging the concepts of opposition-based learning and migration for the selection of good candidates and to maintain diversity, respectively. A multiobjective IIR digitallter design problem takes into consideration magnitude and phase response of thelter simultaneously, while satisfying stability constraints on the coefficients of thelter. The order of thelter is controlled by a control gene whose value is also along withlter coefficients, to obtain the optimum order of the designed IIRlter. Results illustrate that ETLBO is more capable and efficient in comparison to other optimization methods for the design of all types oflter, i.e. high-pass, low-pass, band-stop, and band-pass IIR digitallters.